Course plan

Course literature and supplementary reading

D. Montgomery, E. Peck, G. Vining: Introduction to Linear Regression Analysis. Wiley-Interscience, (6th Edition (2021). ISBN-10: 978-1-119-57872-7. 704 pages or 5th Edition (2012). ISBN-10: 978-1-118-62736-5. 645 pages). Acronym below: MPV.

The textbook MPV can be bought at THS Kårbokhandel, Drottning Kristinas väg 15-19. The book also has a solutions manual.

There are a number of other books that cover the topics of the course, and which we will use during the course. Here are some recommendations, which are all available freely online:

G. James, D. Witten, T. Hastie, R. Tibshirani: An introduction to Statistical Learning Links to an external site. by the publisher Springer.
A. J. Izenman: Modern Multivariate Statistical Techniques. Regression, Classification, and Manifold Learning Links to an external site. by the publisher Springer. Acronym below: Iz.
T. Hastie, R. Tibshirani, J. Friedman: The Elements of Statistical Learning Links to an external site.. Springer, 2ed Edition, 2017. Acronym below: HTF.
T. Hastie, R. Tibshirani, M Wainwright: Statistical Learning with Sparsity: The Lasso and Generalizations Links to an external site. by the publisher Chapman and Hall Books, 2016. Acronym below: HTW.
Practical regression with R Links to an external site. by Julian R. Faraway (2002) with R-code. Links to an external site.

Preliminary plan of lectures and exercises sessions

The order of lectures and exercise sessions is subject to change.
Lecturers and guest lecturers: Timo Koski (TK), Isaac Ren (IR), guest lecturers from If P&C Insurance (If), Mattias Villani (MV).
Problems to be solved during the exercise sessions and recommended exercises to be solved on your own are found here.

Day	Date	Time	Hall	Topic	Lecturer
1. Wed	18/1	15-17	F1	Lecture 1: Introduction to Course Work. Simple Linear Regression, Conditional Expectation MPV parts of Ch. 2 or pp 12-22, p. 26	TK
2. Thu	19/1	10-12	D1	Lecture 2: Centering matrix, idempotent matrices and other linear algebra, random vectors, expectation of random vectors, covariance matrix, multivariate normal distribution.	TK
3. Fri	20/1	8-10	F2	Exercise 1	IR
4. Tue	24/1	13-15	F2	Lecture 3: Multiple Linear regression Part 1. Least Squares Estimate (LSE) Projection geometry of LSE, MLE MPV Ch. 3 pp. 67-83	TK
5. Wed	25/1	10-12	F2	Lecture 4: Multiple Regression Part 2. Gauss-Markov Theorem, Prediction, Quadratic Forms Fundamental Variance Decomposition, Distribution of LSE Residuals	TK
6. Thu	26/1	8-10	D1	Lecture 5: Confidence Intervals for Multiple Regression, F -test for model	TK
7. Fri	27/1	10-12	D1	Exercise 2	IR
8. Tue	31/1	15-17	D1	Lecture 6: Further Confidence Intervals using the centered model MPV pp. 84-85, pp. 581-582	TK
9. Wed	1/2	10-12	F2	Exercise 3	IR
10. Thu	2/2	8-10	E1	Lecture 7: Model Selection by F-test, Variable Selection. Model selction by Akaike Information Criterion (AIC) MPV Ch. 10	TK
11. Tue	7/2	15-17	D1	Exercise 4	IR
12. Wed	8/2	15-17	D1	Lecture 8: Bias-Variance Trade Off, High dimensional Data	TK
13. Thu	9/2	8-10	D1	Lecture 9: 9.1. Bayesian Inference 9.2 Causality and Regression	TK
14. Mon	13/2	10-12	F2	Lecture 10: Bayesian Regression	MV
15. Wed	15/2	10-12	F2	Lecture 11: Model Validation MPV Ch. 11	TK
16. Thu	16/2	8-10	D1	Lecture 12: Big data: reduction of variables by Principal Component Regression	TK
17. Fri	17/2	13-15	E1	Lecture 13: Logistic Regression MPV Ch. 13.2	TK
18. Tue	21/2	15-17	D1	Exercise 5	IR
19. Wed	22/2	10-12	F1	Lecture 14	If
20. Thu	24/2	8-10	E1	Lecture 15	If
21. Mon	27/2	13-15	F1	Exercise 6	If
22. Tue	28/2	15-17	D1	Exercise 7	If
23. Thu	2/3	8-10	D1	Lecture 16	If

Mon	13/3	08-13		Exam
Wed	7/6	08-13		Re-exam